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Record W355090155 · doi:10.1155/2007/571598

Pain in Older Persons

2007· article· en· W355090155 on OpenAlexaffabout
Thomas Hadjistavropoulos

Bibliographic record

VenuePain Research and Management · 2007
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMedicinePsychologyMEDLINEGerontologyPhysical medicine and rehabilitationPhysical therapyPsychiatryBiology

Abstract

fetched live from OpenAlex

It gave me great pleasure to accept the invitation to serve as guest editor for this special issue, which recognizes the International Association for the Study of Pain (IASP) Global Year Against Pain in Older Persons. In 2001, I had the pleasure of guest editing another special issue of Pain Research & Management on Pain and Aging. My 2001 editorial (1) recognized the increasing interest that researchers and clinicians were showing in the topic of pain in older adults, and pointed out as evidence of that interest the efforts to create an IASP Special Interest Group on Pain in Older Persons, the publication of several related volumes (2–4) and the United States Joint Commission on Accreditation of Health Care Organisations guidelines for assessing geriatric pain (5). We have come such a long way in the six years that have passed since the 2001 special issue. The IASP Special Interest Group on Pain in Older Persons is now well established and the IASP has named 2006/2007 as its Global Year Against Pain in Older Adults. In addition, specialized and up-to-date volumes on the topic continue to be published (6,7) and tremendous progress has been made in both the areas of pain assessment (6,8–10) and management (6). The present issue of Pain Research & Management is well balanced. The contributing authors represent a variety of clinical and research disciplines (eg, nursing, medicine and clinical psychology). Two of the four original papers (11,12) are empirical while the other two (13,14) are systematic literature reviews. Two papers focus primarily on older adults residing in long-term care facilities (11,13), one paper focuses on palliative care (14) and one on seniors undergoing total knee arthroplasty (12). This special issue also includes a review of the book Clinical Management of the Elderly Patient in Pain (7). The review was prepared by Romayne Gallagher. Last but not least, Laurence Jerome contributed a case study of an older woman with a 20-year history of vulvodynia (15). The paper by Lucia Gagliese and colleagues (14) helps clarify some of the questions that exist concerning age-related patterns in the relationship between cancer pain and depression. The study by Maya Roth et al (12) helps fill a gap in the literature by examining some of the psychosocial determinants of acute postoperative total knee arthroplasty pain among seniors. In the area of long-term care, the investigation by Sandra Zwakhalen and colleagues (11) provides evidence of knowledge gaps among nursing staff. Such knowledge gaps often relate to insufficient information about assessment tools that have been specifically designed for older persons who have limited ability to communicate due to dementia (16). Systematic information comparing such specialized assessment tools is needed not only by clinicians but also by researchers. Information allowing comparisons across pain assessment tools for patients with dementia is provided by Michele Aubin and colleagues (13). The Aubin et al article also marks an important milestone for the Journal because it is written in French. Although Pain Research & Management has the mandate to publish papers in both French and English, this is the first time that a paper in French has been published. I thank the Editor-in-Chief of Pain Research & Management, Dr Kenneth D Craig, for encouraging this initiative. I extend thanks to all contributors for their important work in this area, and for sharing their findings and ideas with the 18,000 readers of Pain Research & Management (the journal of the Canadian Pain Society). I also extend my thanks to Kenneth D Craig for the opportunity to guest edit this special issue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.370
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2007
Admission routes2
Has abstractyes

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